A video can be defined as a sequence of a plurality of frames or fields being displayed at a certain frame rate. During the early days of video production technology, a video was captured and broadcasted using a tape based media. With the development of computer related technologies and substantial reduction in the cost related to storage memory and processing power, the tape based media began to be replaced by a file based technology. The adoption of file-based media has spearheaded many advantages for broadcasters. There is ease of media storage and retrieval. There is the enhanced flexibility, speed, and sophistication of non-linear editing. File-based content has even changed the way media is delivered. In the tape-based workflows, many of the processes were handled manually and were cumbersome. With the newfound flexibility of file-based flows, the media files are being compressed and formatted using a wide range of compression technologies, file format types, and delivery formats.
With the flexibility of digital data, complex operations are possible to create various types of output files using sophisticated editing techniques, repurposing, and transcoding. The increase in complex operations on media files increases the possibility of injecting errors into the content. These errors can manifest in metadata of file formats. There may be errors with respect to the non-conformance to variety of compression standards. There may be errors related to degradation of video and audio quality. There may also be errors introduced during digitization of tape-based media. In addition, the advancement of HDTV has led to the consumer being media quality aware and more concerned in terms of the value for their money. In traditional tape-based workflows, the concept of quality control (or QC) had a specific point of applicability and in the context of a specific activity. With file-based workflows, the concept of quality expands to content readiness. Content readiness spans multiple points of applicability in multiple contexts of activities across the content lifecycle. The disruption in content lifecycle due to file-based workflows now expands beyond faster lifecycle and new transformation activities to a third dimension—that of content readiness.
Broadly the video quality issues can be divided into three categories namely sequence error, global error in a frame, and a localized frame error. Sequence error means that there is an issue with the sequence in which the frames are displayed. Global error in a frame means that a major part of the video frame has error; a few examples are Corrupted blocks, luminance/chrominance noise, distorted content, Bit/Packet loss, partial or complete temporal loss. Localized Frame Error means that a few areas of the frame have error; a few examples are block error, misplaced block, DC Coefficient error, Slice/Line error, Scratches and Blotches. Such errors may also be termed as an artifact. There exist a few methods for identifying various types of artifacts that may exist in a video frame. Such methods are generally referred to as Bottom-up Approach as they are detection methods for detecting specific type of issues. In the Bottom-up Approach each issue is handled independently with the advantage of a relatively less number of false positives. Though such an approach is able to detect errors, however, there are certain issues with the approach. Since, the bottom up approach is concerned only with specific types of errors, the approach is unable to handle new types of errors when they arise and lacks global perspective. Although, false positives in a Bottom-up Approach are low but false negatives are very high. Moreover, since the Bottom-up Approach caters to individual issues, its performance degrades with every enhancement to handle a new case.
There is an increasing uncertainty in the quality of content that reaches the consumer. The consumer expectation of HD video and the increasing competition in media industry further add to the quality requirements of media content. Therefore, file-based broadcasting has to deal with constantly evolving technical complexities on one hand and expectations of quality by the consumers on the other. In this scenario, the legacy methods of quality control are inadequate. Manual checks become inconsistent, subjective, and difficult to scale in a globally accessible media workflow. Hence, there exists a need for an error detection approach that is robust and effectively identifies various types of errors that may exist in a video file.